{"id":"W4402011517","doi":"10.32604/iasc.2023.034029","title":"Ensemble Modeling for the Classification of Birth Data","year":2024,"lang":"en","type":"article","venue":"Intelligent Automation & Soft Computing","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; New Brunswick Innovation Foundation","keywords":"Ensemble learning; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001586138,0.000118959,0.0001327266,0.0001391815,0.0002368053,0.0002792197,0.001474375,0.00005288472,0.000005449939],"category_scores_gemma":[0.0004722653,0.00009260221,0.0000629032,0.0004662755,0.00002226323,0.0003659564,0.0004324341,0.0001974992,0.00002869981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005816501,"about_ca_system_score_gemma":0.0001492317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000642899,"about_ca_topic_score_gemma":0.00001138775,"domain_scores_codex":[0.9983591,0.00009734294,0.0005589908,0.0004608724,0.0003098145,0.0002139128],"domain_scores_gemma":[0.9969939,0.001598586,0.0001661132,0.000994693,0.0002096391,0.00003706589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001843819,0.00001224451,0.0001440749,0.000301361,0.00002265035,4.220103e-7,0.001881724,0.2836001,0.0003077315,0.06906097,0.0003158534,0.6443511],"study_design_scores_gemma":[0.00003167716,0.00002389475,0.0002528217,0.0001618636,0.000008713274,0.000006419959,0.00009839382,0.9909588,0.0002723034,0.002665838,0.005426968,0.0000923202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007280035,0.001072844,0.986231,0.003177588,0.001003445,0.0004359072,0.000008161996,0.0007002402,0.00009072384],"genre_scores_gemma":[0.9155774,0.00001582617,0.08404453,0.000084853,0.0001775709,0.00001073961,0.00003260454,0.00001577682,0.00004068358],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9082974,"threshold_uncertainty_score":0.3776208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1318125959145786,"score_gpt":0.3785556182336461,"score_spread":0.2467430223190674,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}